Cited by 264Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIE-Government and Public Services

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The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making

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de Bruijn, Hans, Warnier, Martijn, Janssen, Marijn·2022·Government Information Quarterly

DOI 10.1016/j.giq.2021.101666

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Abstract

Governments look at explainable artificial intelligence's (XAI) potential to tackle the criticisms of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a solution for automated decisions, the wicked nature of the challenges governments face complicates the use of XAI. Wickedness means that the facts that define a problem are ambiguous and that there is no consensus on the normative criteria for solving this problem. In such a situation, the use of algorithms can result in distrust. Whereas there is much research advancing XAI technology, the focus of this paper is on strategies for explainability. Three illustrative cases are used to show that explainable, data-driven decisions are often not perceived as objective by the public. The context might raise strong incentives to contest and distrust the explanation of AI, and as a consequence, fierce resistance from society is encountered. To overcome the inherent problems of XAI, decisions-specific strategies are proposed to lead to societal acceptance of AI-based decisions. We suggest strategies to embrace explainable decisions and processes, co-create decisions with societal actors, move away from an instrumental to an institutional approach, use competing and value-sensitive algorithms, and mobilize the tacit knowledge of professionals

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Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
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Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
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Fairness and Abstraction in Sociotechnical Systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287598 · Cited by 1,631
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence ClassificationPreprint
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Techniques for interpretable machine learning
Du, M., Liu, N., & Hu, X. (2019) · Communications of the ACM · doi:10.1145/3359786 · Cited by 1,268
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
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Value-sensitive design
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Show all 37 references

Research works printed but not linked: 2

LeCun, 2015, Deep learning., 521, 436
McDonald, 2019, AI system for granting UK visas is biased, rights groups claim, The Gaurdian, 29

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Authors: de Bruijn, H., Warnier, M., & Janssen, M.
Affiliations: Delft University of Technology (NL)
Corresponding author: Janssen, Marijn
ORCID: Warnier, Martijn
Janssen, Marijn
Venue: Government Information Quarterly
Publisher: Elsevier BV
Published: 2022-04
Volume: 39
Issue: 2
Pages: 101666
ISSN: 0740-624X (ISSN-L)
DOI: 10.1016/j.giq.2021.101666
OpenAlex: W4205177168
Cited by: 264
References: 37
Citations by year: 2026: 66 · 2025: 74 · 2024: 63 · 2023: 44 · 2022: 15
FWCI: 10.87
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Government Information Quarterly (publishedVersion, cc-by)
Lirias (KU Leuven) (submittedVersion, cc-by)
Research Repository (Delft University of Technology) (submittedVersion, cc-by)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Social Sciences › Political Science and International Relations › E-Government and Public Services
Keywords: explainable AI, algorithmic decision-making, algorithmic transparency, public trust in AI, wicked problems, automated decision-making, government AI policy

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The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making

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